Top Free Annotation Tools for Data Labeling

If you’re diving into machine learning or AI, you’ll quickly realize that data labeling is a crucial step. Annotation tools help you label your images, videos, and other datasets, making them ready for training your models. While many annotation tools come with a hefty price tag, there are some great free options out there. These free tools can help you get the job done without breaking the bank, whether you’re working on a small project or need a scalable solution. Let’s take a look at the best free annotation tools available and how they can help streamline your data labeling process.

1. FlyPix AI

At FlyPix AI, we specialize in geospatial analysis by leveraging artificial intelligence to turn satellite and aerial imagery into actionable insights. The platform is designed to help us detect objects, monitor changes, and identify anomalies, which is crucial for industries like agriculture, urban planning, and environmental monitoring. Although FlyPix AI is a paid platform, it provides valuable features that can be useful as free annotation tools for smaller projects or teams just starting with geospatial data.

We value FlyPix AI for its no-code platform that allows us to easily create and train custom AI models. The interactive annotation tools help us annotate imagery and train models without needing coding skills, making it an accessible tool for both technical and non-technical users. Additionally, FlyPix AI’s real-time analytics and heatmap generation help us track changes over time, providing clear and actionable data insights.

Key Features:

• No-code platform for image annotation and model training
• Supports satellite, drone, and LiDAR data types
• Interactive tools for annotating objects and tracking changes
• Real-time analytics with heatmap generation
• Enterprise-grade support for large-scale projects

Services:

• Geospatial object detection and localization
• Detecting and tracking changes or anomalies in images
• Custom AI model development for your unique project needs
• Integration with GIS systems for a smoother workflow
• Visualizing data patterns with heatmaps

Best For:

• Teams working with satellite, drone, or LiDAR imagery
• Projects in agriculture, urban planning, and environmental monitoring
• Users looking for a no-code tool to quickly annotate and train models
• Anyone needing real-time change tracking and visual data representation

Contact and Social Media Information:

• Website: flypix.ai
• Address: Robert-Bosch-Str. 7, 64293 Darmstadt, Germany
• Phone: +49 6151 2776497
• Email: [email protected] 
• LinkedIn: www.linkedin.com/company/flypix-ai

2. CVAT

CVAT (Computer Vision Annotation Tool) is an open-source platform designed for annotating images and videos, mainly for computer vision tasks. It’s a popular choice among teams working on machine learning and AI projects that require object detection, segmentation, and tracking. The platform supports various annotation formats and provides a user-friendly interface, making it a go-to tool for those looking to label large datasets efficiently.

CVAT stands out with its focus on flexibility and collaboration. Teams can easily work together on annotation projects, and the platform integrates well with machine learning workflows, allowing users to export annotated data for further processing. Whether you’re working on a research project or need to label data for commercial applications, CVAT provides the necessary tools to handle complex annotation tasks.

Key Features:

• Open-source and customizable
• Supports object detection, segmentation, and tracking
• Real-time collaboration for team-based annotation
• Integration with machine learning pipelines
• Works with various image and video formats

Best For:

• AI and machine learning teams needing accurate data labeling
• Large-scale annotation projects requiring team collaboration
• Companies looking for a free, open-source tool
• Research teams focused on computer vision tasks

Contact and Social Media Information:

• Website: www.cvat.ai
• LinkedIn: www.linkedin.com/company/cvat-ai
• Facebook: www.facebook.com/cvat.corp

3. Label Studio

Label Studio is an open-source tool that provides flexible data labeling for images, text, audio, and video. It’s a great option for machine learning projects where you need to annotate diverse data types, including both structured and unstructured data. Label Studio is designed to be user-friendly while also offering the power of customization, making it a solid choice for teams with unique annotation requirements.

This tool supports a wide range of data types and formats, allowing teams to annotate data for projects involving computer vision, natural language processing, and other AI-related tasks. Its open-source nature means it’s cost-effective and customizable, so users can adapt it to meet their specific project needs without needing a hefty budget.

Key Features:

• Open-source and highly customizable
• Supports image, video, text, and audio annotation
• Real-time collaboration for team-based work
• Seamless integration with machine learning workflows
• Easy-to-use interface for efficient data labeling

Best For:

• Teams needing to label diverse data types for machine learning
• Companies looking for a free, open-source tool with customization options
• Projects involving computer vision, NLP, and AI
• Teams working on data labeling tasks that require real-time collaboration

Contact and Social Media Information:

• Website: labelstud.io
• LinkedIn: www.linkedin.com/company/heartex
• Twitter: x.com/labelstudiohq

4. LabelMe

LabelMe is a simple, open-source tool for annotating images. It’s particularly useful for teams that need to quickly label data for machine learning tasks like object detection and segmentation. LabelMe supports a variety of annotation formats, including bounding boxes and polygons, making it flexible enough for different types of projects.

Though it is a relatively basic tool, LabelMe’s simplicity and accessibility make it an ideal choice for small-scale projects or researchers working on a budget. It’s open-source, which means anyone can use it for free, and it’s easy to get started with, making it a practical solution for teams that don’t need the complexity of larger annotation tools.

Key Features:

• Open-source and free to use
• Supports bounding boxes, polygons, and other annotations
• Simple interface for quick image labeling
• Great for object detection and segmentation tasks
• Customizable for specific annotation needs

Best For:

• Small-scale image annotation projects
• Researchers or teams on a budget needing a quick solution
• Projects involving object detection and segmentation
• Users looking for an open-source, no-cost annotation tool

Contact and Social Media Information:

• Website: labelme.io
• E‑mail: [email protected]
• Twitter: x.com/labelmeai

5. Scalabel

Scalabel is a flexible, open-source image annotation tool that’s designed for large-scale projects. It supports a variety of annotation types like bounding boxes, keypoints, and segmentation, and it’s ideal for machine learning tasks that require precise, high-quality labels. Scalabel is built to scale, so it works well for both small and large datasets.

One of the features that sets Scalabel apart is its collaboration capabilities, which allow teams to work together on annotation tasks in real time. Whether you’re working with a small team or need a tool that can handle enterprise-level datasets, Scalabel provides a reliable solution for efficient data labeling.

Key Features:

• Open-source and scalable for large datasets
• Supports bounding boxes, keypoints, and segmentation
• Real-time collaboration for team-based annotation
• Customizable for specific project requirements
• Integrates with machine learning workflows

Best For:

• Teams working with large, complex datasets
• Projects requiring high-quality image annotation
• Machine learning teams focused on object detection and segmentation
• Collaborative projects that need team-based annotation features

Contact and Social Media Information:

• Website: www.scalabel.ai

6. VGG Image Annotator (VIA)

VGG Image Annotator (VIA) is a simple, open-source tool developed by the Visual Geometry Group at the University of Oxford. It’s designed for labeling images and videos, making it a solid choice for teams working on machine learning and AI tasks like object detection and segmentation. VIA runs directly in the browser, so there’s no need for installation, making it incredibly easy to access and start using right away.

What sets VIA apart is its simplicity and accessibility. It’s not overloaded with unnecessary features, which makes it perfect for smaller projects or teams that just need a straightforward tool for annotation. While it may lack the advanced features of some other tools, it provides the essential functionalities needed for quick, efficient labeling, especially for researchers and developers who need a no-frills tool.

Key Features:

• Open-source and browser-based
• Supports object detection and segmentation tasks
• Simple, user-friendly interface
• No installation required
• Ideal for small-scale annotation projects

Best For:

• Small-scale image annotation projects
• Researchers or developers needing a simple, accessible tool
• Teams looking for an open-source, no-cost solution
• Users who prefer a browser-based tool with no installation

Contact and Social Media Information:

• Website: www.robots.ox.ac.uk/~vgg/software/via
• E‑mail: [email protected]
• Twitter: x.com/Oxford_VGG

7. MONAI Label

MONAI Label is an open-source image annotation tool built for medical imaging tasks. It’s part of the larger MONAI framework, which focuses on AI in healthcare. This tool helps researchers and healthcare teams annotate medical images for projects like segmentation, classification, and detection. MONAI Label integrates seamlessly with medical imaging software, which makes it especially useful for teams working in healthcare or medical research.

MONAI Label’s strength lies in its focus on healthcare. The platform is built specifically for medical image annotation, allowing professionals to annotate medical scans or radiology images with ease. With its open-source nature, MONAI Label provides a flexible, cost-effective solution for teams needing precise labeling for medical AI models.

Key Features:

• Open-source tool specifically for medical image annotation
• Supports segmentation, classification, and detection tasks
• Integrates with popular medical imaging software
• Focused on healthcare and medical research applications
• Customizable for specific medical use cases

Best For:

• Healthcare and medical research teams working with medical imaging
• Projects requiring precise annotation of medical images
• Teams needing an open-source, customizable annotation tool for healthcareOrganizations integrating annotation into their medical imaging workflows

Contact and Social Media Information:

• Website: monai.io
• LinkedIn: www.linkedin.com/company/projectmonai
• Twitter: x.com/ProjectMONAI

8. Annotely

Annotely is an image annotation software that allows teams to label images for machine learning and AI projects. It offers a simple, intuitive interface for tasks like object detection and image segmentation. Annotely stands out for its straightforwardness, making it easy for users to jump straight into annotating images without dealing with complicated setups or steep learning curves.

This tool is particularly suited for small to medium-scale projects, and it’s a great option for businesses or individuals who need a reliable, no-cost solution for data labeling. Annotely provides all the basic features needed for image annotation, and its flexibility makes it adaptable for different types of AI and machine learning applications.

Key Features:

• Simple and intuitive user interface
• Supports bounding box and polygon annotation
• Ideal for object detection and segmentation tasks
• Customizable for different annotation needs
• Free and accessible for small to medium-scale projects

Best For:

• Small to medium teams needing a quick and easy annotation tool
• Projects involving object detection and segmentation
• Users who want an accessible, no-cost tool for labeling images
• Businesses needing a reliable image annotation software

Contact and Social Media Information:

• Website: annotely.com
• Twitter: x.com/@annotely

9. Supervisely

Supervisely is used as a workspace where annotation sits next to data organization and model experimentation. When people approach it as a free annotation tool, they usually do so through its trial access or community usage, mainly to label images, videos, medical scans, or 3D data. The annotation interface itself is flexible and supports both simple and more detailed labeling tasks without forcing a rigid workflow.

Annotation here is not treated as a one-off step. Labels can be reviewed, adjusted, and reused as datasets evolve. For smaller projects or research work, the free access is often enough to explore advanced annotation types and understand how larger pipelines might be structured later.

Key Highlights:

• Image, video, medical, and 3D annotation support
• Tools for boxes, polygons, keypoints, tracking, and segmentation
• Optional AI-assisted labeling for repetitive tasks
• Browser-based interface with cloud or self-hosted options
• Project-level collaboration and review

Who it’s best for:

• Teams testing complex annotation setups
• Research projects with non-standard data types
• Users combining annotation with dataset organization
• Small-scale work that needs flexibility over simplicity

Contact and Social Media Information:

• Website: supervisely.com
• E-mail: [email protected]
• Facebook: x.com/@supervisely_ai
• LinkedIn: www.linkedin.com/company/10456352

10. Latitude

Latitude is used for annotating text outputs from large language models rather than visual data. The free access is commonly used to add human feedback to model responses, conversations, or specific text spans. Instead of creating static datasets, annotation here helps teams understand how models behave in real use.

The tool fits into workflows where evaluation and iteration matter more than volume labeling. Feedback can be applied directly to generated outputs, making it easier to spot patterns, failures, or inconsistencies. For early-stage LLM projects, this kind of annotation often replaces custom internal tools.

Key Highlights:

• Annotation of text and conversation outputs
• Span-level and response-level feedback
• Focus on human judgment rather than bulk labeling
• Works alongside existing LLM pipelines
• Free access suitable for experimentation

Who it’s best for:

• Teams building or testing LLM-based systems
• Projects needing structured human feedback
• Prompt evaluation and iteration workflows
• Small groups without custom evaluation tooling

Contact and Social Media Information:

• Website: latitude.so

11. Roboflow

Roboflow is commonly used as a starting point for image annotation in computer vision projects. Through its free tier, users can label images directly in the browser using familiar tools like boxes and polygons. The setup is straightforward, which lowers the barrier for people new to annotation work.

Beyond drawing labels, datasets can be organized, reviewed, and lightly processed in the same place. While larger teams often move to paid plans, the free option is enough for learning, prototyping, and building early datasets without installing local software.

Key Highlights:

• Browser-based image annotation
• Bounding boxes, polygons, keypoints, and classification
• Optional AI-assisted labeling
• Dataset organization and review tools
• Simple collaboration features

Who it’s best for:

• Beginners learning computer vision annotation
• Students and early-stage projects
• Small teams labeling image datasets
• Users who want quick setup and minimal overhead

Contact and Social Media Information:

• Website: roboflow.com
• Twitter: x.com/roboflow
• LinkedIn: www.linkedin.com/company/roboflow-ai

12. Doccano

Doccano is an open-source tool focused on text annotation tasks such as classification, sequence labeling, and named entity recognition. It is usually self-hosted, which gives teams full control over data and annotation rules. Because it is free and open source, it is often chosen for long-term NLP projects with specific requirements.

The interface keeps things practical and task-focused. Annotators work through text items, apply labels, and export results in common formats. While setup requires some technical effort, the trade-off is flexibility and no usage limits once running.

Key Highlights:

• Fully open-source and free
• Text classification and sequence labeling support
• Web-based annotation interface
• Export to standard NLP formats
• Self-hosted for data control

Who it’s best for:

• NLP teams working with text datasets
• Research groups needing open-source tools
• Organizations with strict data requirement
• Projects needing custom annotation schemes

Contact and Social Media Information:

• Website: github.com/doccano/doccano
• Facebook: www.facebook.com/GitHub
• Twitter: x.com/github
• LinkedIn: www.linkedin.com/company/github

13. LightTag

LightTag is used for text annotation in NLP workflows where speed and consistency matter. It is often referenced in research and applied settings as a practical way to label text while keeping annotation tied to downstream tasks. Free or low-barrier access is typically used for experimentation, teaching, or smaller projects.

Annotation focuses on tagging spans of text and managing review cycles rather than bulk labeling. The design encourages annotators and reviewers to work closely, which helps maintain consistency across datasets as they grow or change.

Key Highlights:

• Span-based text annotation
• Designed around NLP workflows
• Review and consistency support
• Lightweight setup for small projects
• Commonly used in research contexts

Who it’s best for:

• NLP researchers and students
• Named entity recognition and tagging tasks
• Teams valuing consistency over scale
• Text annotation tied to model development

Contact and Social Media Information:

• Website: aclanthology.org

14. tagtog 

tagtog is used for text annotation when the goal is to build structured NLP datasets without getting buried in setup or tooling decisions. It supports manual labeling of entities, relations, and document-level tags, and it can also handle native PDF files, which is still uncommon among free annotation tools. Free usage is often enough for small teams, research work, or early-stage dataset creation.

The annotation editor is designed around reading flow, not form-heavy interfaces. Multiple annotators can work on the same documents, compare versions, and resolve differences through review and adjudication. Automatic suggestions can be added later, but annotation still works well even when kept fully manual.

Key Highlights:

• Text and PDF annotation in the browser
• Entity, relation, and document-level labels
• Supports overlapping and normalized annotations
• Multi-user workflows with review and adjudication
• Cloud-based with on-premises option

Who it’s best for:

• NLP projects focused on text or documents
• Teams involving subject matter experts
• Annotation tasks needing review and agreement checks
• Projects that mix manual and assisted labeling

Contact and Social Media Information:

• Website: docs.tagtog.com

15. QSL

QSL is a lightweight, open-source labeling tool that runs inside Jupyter environments. It is often used when annotation needs to stay close to code, experiments, or notebooks. The tool supports images, videos, and even time-series data, which makes it useful for research-heavy workflows.

Instead of a full web platform, QSL works as a widget or command-line tool. Labels are saved directly to files, keeping the process simple and transparent. Because it is still evolving, it fits best where flexibility and direct control matter more than polish.

Key Highlights:

• Open-source and free
• Runs as a Jupyter widget or CLI
• Supports image, video, and time-series labeling
• Bounding boxes, polygons, masks, and ranges
• Local or cloud-based media loading

Who it’s best for:

• Researchers working in Jupyter notebooks
• Small experiments and custom workflows
• Time-series or mixed data annotation
• Users comfortable with code-driven tools

Contact and Social Media Information:

• Website: github.com/faustomorales/qsl
• Facebook: www.facebook.com/GitHub
• Twitter: x.com/github
• LinkedIn: www.linkedin.com/company/github

16. ELAN

ELAN is a desktop annotation tool widely used in linguistics and behavioral research. It focuses on aligning annotations with audio and video timelines, making it suitable for speech, gesture, and interaction analysis. The software is free to download and runs locally on major operating systems.

Annotations in ELAN are organized into time-aligned tiers, which allows detailed and layered labeling. While the interface is more technical than web-based tools, it offers strong control over temporal structure and is commonly used in academic environments.

Key Highlights:

• Free desktop application
• Audio and video annotation with timelines
• Tier-based annotation structure
• Supports complex temporal relationships
• Works offline on local machines

Who it’s best for:

• Linguistic and speech research
• Multimodal audio-video analysis
• Academic and research settings
• Projects needing precise time alignment

Contact and Social Media Information:

• Website: archive.mpi.nl
• Address: The Language Archive, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525XD Nijmegen, The Netherlands

17. Yedda

Yedda is associated with visual data annotation for images and videos, usually in applied and operational contexts. While much of its work centers on managed services, annotation tools are also used internally to support labeling workflows. Free usage is more limited and typically applies to small trials or exploratory work.

The annotation focus is on visual actions and events rather than dataset experimentation. As a result, it is less commonly used as a standalone free tool and more often appears in projects where annotation feeds directly into applied AI systems.

Key Highlights:

• Image and video annotation support
• Focus on real-world visual scenarios
• Collaborative annotation workflows
• Used alongside applied AI projects
• Limited free access for small-scale use

Who it’s best for:

• Teams exploring visual annotation workflows
• Early testing before managed annotation
• Projects tied to operational video data
• Users evaluating annotation approaches

Contact and Social Media Information:

• Website: www.yedda.ai
• E-mail: [email protected]
• Address: 10 Anson Road #22-02 International Plaza, Singapore 079903
• Phone: +65 6950 4154

18. Diffgram

Diffgram is an open source tool that brings labeling and training data management under one roof. It can be installed on your own servers or accessed through hosted setups, and it supports annotation of images, video, text, audio, 3D data, and more. The interface is designed to let annotators work directly on tasks and then manage those annotations as datasets grow.

Since Diffgram also integrates data workflows and basic automation, labeling becomes part of a larger loop where you keep track of how data moves through a project. Teams can invite others to work on tasks, review or correct labels, and mix manual annotation with automated suggestions. 

Key Highlights:

• Open source and self-hosted annotation platform
• Supports images, video, text, audio, and 3D data types
• Combines annotation with data management and workflows
• Multi-user support for collaborative labeling
• Can integrate automation and model predictions

Who it’s best for:

• Teams that want full control over their annotation data
• Projects involving mixed data types (visual, text, audio)
• Users who prefer open-source tools over cloud services
• Small to mid-scale ML annotation efforts

Contact and Social Media Information:

• Website: github.com/diffgram/diffgram
• Facebook: www.facebook.com/GitHub
• Twitter: x.com/github
• LinkedIn: www.linkedin.com/company/github

Conclusion

Choosing the right free annotation tools is crucial for streamlining data labeling in machine learning and AI projects. These tools simplify the process of preparing your datasets, whether for object detection, image segmentation, or other AI tasks. From basic open-source solutions to more feature-rich platforms, there are options available for every need and project size.

The best tools not only speed up annotation but also ensure the accuracy of the data, which is essential for building reliable AI models. With many tools offering collaborative features and integration capabilities, teams can efficiently work together on large datasets. No matter your project’s scale or complexity, the right annotation tool helps save time and ensures that the data is ready for training your models.